More than 1,200 cities worldwide are embarking on low carbon goals. However, currently there are no protocols in place to consistently account for GHG emissions associated with cities. Thus, this thesis explores mathematical relationships, approximations, implementation challenges, and policy relevance for three city-scale GHG emission accounting methods: Purely Territorial, Trans-Boundary Infrastructure Supply-Chain Footprint (TBIF), and Consumption-Based Footprint (CBF). Mathematical relationships using Single-Region Input-Output, and Multi-Region Input-Output models showed that neither TBIF nor CBF provided a more holistic accounting of trans-boundary GHG. A typology of cities defined as: net-producers, net-consumers, and trade-balanced in terms of their GHGs embodied in trade is important for understanding the trans-boundary supply-chains serving cities. Data inputs for TBIF are found to be more robust and readily available, compared to CBF. A meta-analysis of 21 US cities showed that trans-boundary electricity generation, air travel, fuel refining, along with the production of food, cement, and iron & steel, may be well-suited for allocation to cities based on their use in city-wide residential-commercial-industrial activities in the TBIF method. Territorial GHGs captured as little as 37% of the total (in-boundary plus trans-boundary) footprint for net-consumer cities, and as large as 68% for net-producers. On average, TBIF captured 75% (n=2) of the total footprint for net-producers, 63% (n=11) for trade-balanced, and 62% (n=8) for net-consumer cities. In contrast, CBF captured an average of 35% (n=2), 57% (n=11), and 71% (n=8) of the total footprint for net-producers, trade-balanced, and net-consumer cities, respectively. Various metrics of GHG emissions computed for the three methods were assessed for their ability to appropriately compare cities'. For territorial GHG, neither GHGTerritorial/capita nor GHGTerritorial/GDP reflected urban efficiency of cities. For TBIF, GHGTBIF/GDP with only electricity allocated (R2=0.62), and GHGTBIF/GDP with the additional suitable infrastructures allocated (R2=0.77), correlated well with an urban efficiency index (UEI) composed of commercial-industrial production efficiency, household energy efficiency, and transportation system efficiency. However, GHGTBIF/capita showed poor correlation (R2=0.1) with the UEI as expected from a production-based account. In contrast, for CBF, GHGCBF/capita and GHGCBF/GDP showed an improved correlation (R2=0.4) with the UEI. However, GHGCBF/capita correlated more strongly (R2=0.76) with per capita expenditures. These data suggest that GHGTBIF/GDP is the appropriate metric for comparing cities based on their urban efficiency, and that GHGCBF/capita is appropriate for viewing cities from a consumption perspective. For the 21 cities modeled, GHGTBIF/GDP ranged from 154 mt-CO2e/GDP to 747 mt-CO2e/GDP, and GHGCBF/capita ranged from 15 mt-CO2e/cap to 32 mt-CO2e/capita. The TBIF was implemented in Delhi, India to explore issues of data availability and transferability of methods from the US to rapidly industrializing nations. Fieldwork showed sufficient availability and adaptability of TBIF methodology from the US to India yielding GHGTBIF equal to 948 mt-CO2e/GDP in Delhi vs. 413 mt-CO2e/GDP in Denver. Broad energy use metrics between Delhi and Denver help explain differences between the two cities. All GDP in this thesis represent 2008 real USD. Given that TBIF captured the majority of the total GHG footprint (62%-75%) in 21 cities in the meta-analysis, was well correlated with the urban efficiency performance of cities, and could be readily implemented in the US and internationally, this thesis finds TBIF to be well suited for international GHG protocols that aim to compare city-efficiencies.

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